The guidance of metallogenic theory is urgently needed under the background that global mineral exploration is gradually turning to the target at "greenfields", deep earth and coverage areas. The concept of metallogenic system proposed at the end of the last century has attracted extensive attention and study of the mining industry due to its powerful function of regional mineralization forecasting. In this study, first and foremost, the authors review the concept, components and classification of mineral systems. Then the methods of detecting and identifying the main components of the metallogenic system are discussed. Last but not least, the deep process, crustal structure and geophysical response of typical intracontinental metallogenic systems are discussed based on the authors' multi-scale exploration in the middle and lower reaches of the Yangtze River Metallogenic Belt in recent years, and the application of the concept of mineral system in the field of metallogenic prediction is also prospected. The main conclusions of this paper are as follows:(1) The mineral system is a natural system that comprises all the essential factors controlling the formation and preservation of deposits, with basic components of "source", "path" and "site". Each component includes complex physical, chemical and kinetic processes. (2) A deposit is the ‘result’ of multi-scale deep processes coupling at a certain ‘point’ in the mineral system. During the evolution of the mineral system, various physical and chemical processes have strongly "modified" the crust and lithospheric mantle, leaving behind various physical, chemical, and mineralogical "footprints" with significant detectability due to the altered geophysical properties. (3) A new model was proposed based on the multi-scale exploration in the middle and lower reaches of the Yangtze River Metallogenic Belt, for the understanding of "source", "path" and "site" of a typical intracontinentalmetallogenic system. (4) Mineral system based multi-scale target predication will be a prospective research direction in the future, with the continuous developing of geoscience big data, machine learning and artificial intelligence.